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Existing reference-free turn-level evaluation metrics for chatbotsinadequately capture the interaction between the user and the system.Consequently, they often correlate poorly with human evaluations. To addressthis issue, we propose a novel model-agnostic approach that leveragesConditional Pointwise Mutual Information (C-PMI) to measure the turn-levelinteraction between the system and the user based on a given evaluationdimension. Experimental results on the widely used FED dialogue evaluationdataset demonstrate that our approach significantly improves the correlationwith human judgment compared with existing evaluation systems. By replacing thenegative log-likelihood-based scorer with our proposed C-PMI scorer, we achievea relative 62.6% higher Spearman correlation on average for the FED evaluationmetric. Our code is publicly available at https://github.com/renll/C-PMI.

C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation